Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution
In numerous survival analysis experiments, subjects may experience failure or death due to multiple causes. Whether these causes are dependent or independent, this study delves into the competing risk lifetime model under progressively type-II censoring schemes, where removal events follow a binomia...
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IEEE
2025-01-01
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| author | Ahlam H. Tolba Ahmed Ramses El-Saeed |
| author_facet | Ahlam H. Tolba Ahmed Ramses El-Saeed |
| author_sort | Ahlam H. Tolba |
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| description | In numerous survival analysis experiments, subjects may experience failure or death due to multiple causes. Whether these causes are dependent or independent, this study delves into the competing risk lifetime model under progressively type-II censoring schemes, where removal events follow a binomial distribution. Specifically, we focus on the generalized power half logistic geometric lifetime failure model in the context of independent causes. We consider the removal of subjects at each failure time according to a binomial distribution with known parameters. Both classical and Bayesian approaches facilitate point- and interval-estimation procedures for parameters and parametric functions. The Bayesian estimate is derived using the Markov Chain Monte Carlo (MCMC) method, incorporating symmetric and asymmetric loss functions. The Metropolis-Hasting algorithm is applied to generate MCMC samples from the posterior density function. A simulated data set is utilized to evaluate the performance of the two estimation approaches under the proposed censoring scheme. In addition, a real dataset is employed for illustrative purposes. |
| format | Article |
| id | doaj-art-ecf4958100eb4c878ca441b044c79ce2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-ecf4958100eb4c878ca441b044c79ce22025-08-20T03:43:52ZengIEEEIEEE Access2169-35362025-01-0113810028101710.1109/ACCESS.2025.356731010988770Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric DistributionAhlam H. Tolba0https://orcid.org/0000-0001-8938-9187Ahmed Ramses El-Saeed1https://orcid.org/0000-0001-8303-1782Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, EgyptDepartment of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaIn numerous survival analysis experiments, subjects may experience failure or death due to multiple causes. Whether these causes are dependent or independent, this study delves into the competing risk lifetime model under progressively type-II censoring schemes, where removal events follow a binomial distribution. Specifically, we focus on the generalized power half logistic geometric lifetime failure model in the context of independent causes. We consider the removal of subjects at each failure time according to a binomial distribution with known parameters. Both classical and Bayesian approaches facilitate point- and interval-estimation procedures for parameters and parametric functions. The Bayesian estimate is derived using the Markov Chain Monte Carlo (MCMC) method, incorporating symmetric and asymmetric loss functions. The Metropolis-Hasting algorithm is applied to generate MCMC samples from the posterior density function. A simulated data set is utilized to evaluate the performance of the two estimation approaches under the proposed censoring scheme. In addition, a real dataset is employed for illustrative purposes.https://ieeexplore.ieee.org/document/10988770/Competing risks modelgeneralized power half logistic geometric distributionprogressive type II censoringmaximum likelihood estimationBayesian methodreliability analysis |
| spellingShingle | Ahlam H. Tolba Ahmed Ramses El-Saeed Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution IEEE Access Competing risks model generalized power half logistic geometric distribution progressive type II censoring maximum likelihood estimation Bayesian method reliability analysis |
| title | Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution |
| title_full | Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution |
| title_fullStr | Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution |
| title_full_unstemmed | Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution |
| title_short | Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution |
| title_sort | competing risks failure model under progressive censoring with random removals based on the generalized power half logistic geometric distribution |
| topic | Competing risks model generalized power half logistic geometric distribution progressive type II censoring maximum likelihood estimation Bayesian method reliability analysis |
| url | https://ieeexplore.ieee.org/document/10988770/ |
| work_keys_str_mv | AT ahlamhtolba competingrisksfailuremodelunderprogressivecensoringwithrandomremovalsbasedonthegeneralizedpowerhalflogisticgeometricdistribution AT ahmedramseselsaeed competingrisksfailuremodelunderprogressivecensoringwithrandomremovalsbasedonthegeneralizedpowerhalflogisticgeometricdistribution |